Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions

Overlapping Cervical Nuclei Separation using Watershed Transformation and Elliptical Approach in Pap Smear Images

In this study, a robust method is proposed for accurately separating overlapping cell nuclei in cervical microscopic images. This method is based on watershed transformation and an elliptical approach. Since the watershed transformation process of taking the initial seed is done manually, the method was developed to obtain the initial seed automatically. Total initial seeds at this stage represents the number of nuclei that exist in the image of a pap smear, either overlapping or not. Furthermore, a method was developed based on an elliptical approach to obtain the area of each of the nuclei automatically. This method can successfully separate several (more than two) clustered cell nuclei. In addition, the proposed method was evaluated by experts and was proven to have better results than methods from previous studies in terms of accuracy and execution time. The proposed method can determine overlapping and non-overlapping boundaries of nuclei fast and accurately. The proposed method provides better decision-making on areas with overlapping nuclei and can help to improve the accuracy of image analysis and avoid information loss during the process of image segmentation

Classification of squamous cell cervical cytology

Cervical cancer occurs significantly in women in developing countries every day and produces a high number of casualties, with a large economic and social cost. The World Health Organization, in the right against cervical cancer, promotes early detection screening programs by difeerent detection techniques such as conventional cytology (Pap), cytology liquid medium (CML), DNA test Human Papillomavirus (HPV), staining with dilute acetic acid and Lugol's iodine solution. Conventional cytology is the most used technique, being widely accepted, inexpensive, and with quality control mechanisms. The test has shown a sensitivity of 38% to 84% and a specificity of 90% in multiple studies and has been considered as the choice test for screening [14]. The cervical cancer is not a public health problems in developed countries since more than three decades, among others because of implementation of other tests such as the CML which has increased the sensitivity to a figures that vary between 76% and 99 %. This test in particular produces a thin monolayer of cells that are examined. In our countries this technique is really far from being applied because of its high cost. In consequence, the conventional cytology has remained in practice as the only possible examination of the cervix pathology. In this technique, a sample of cells from the transformation zone of the cervix is taken, using a brush or wooden spatula, spread onto a slide and fixed with a preservative solution. This sample is then sent to a laboratory for staining and microscopic examination to determine whether cells are normal or not. This task requires time and expertise for the diagnosis. Attempting to alleviate the work burden from the number of examinations in clinical routine scenario, some researchers have proposed the development of computational tools to detect and classify the cells of the transformation cervix zone. In the present work the transformation zone is firstly characterized using color and texture descriptors defined in the MPEG-7 standard, and the tissue descriptors are used as the input to a bank of binary classifiers, obtaining a precision of 90% and a sensitivity of 83 %. Unlike traditional approaches that extract cell features from previously segmented cells, the present strategy is completely independent of the particular shape. Yet most works in the domain report higher precision rates, the images used in these works for training and evaluation are really different from what is obtained in the cytology laboratories in Colombia. Overall, most of these methods are applied to monolayer techniques and therefore the recognition rates are better from what we found in the present investigation. However, the main aim of the present work was thus to develop a strategy applicable to our real conditions as a pre-screening method, case in which the method should be robust to many random factors that contaminate the image capture. A segmentation strategy is very easily misleaded by all these factor so that our method should use characteristics independently of the segmentation quality, while the reading time is minimized, as well as the intra-observer variability, facilitating thereby real application of such screening tools.Maestrí

Deep learning for digitized histology image analysis

“Cervical cancer is the fourth most frequent cancer that affects women worldwide. Assessment of cervical intraepithelial neoplasia (CIN) through histopathology remains as the standard for absolute determination of cancer. The examination of tissue samples under a microscope requires considerable time and effort from expert pathologists. There is a need to design an automated tool to assist pathologists for digitized histology slide analysis. Pre-cervical cancer is generally determined by examining the CIN which is the growth of atypical cells from the basement membrane (bottom) to the top of the epithelium. It has four grades, including: Normal, CIN1, CIN2, and CIN3. In this research, different facets of an automated digitized histology epithelium assessment pipeline have been explored to mimic the pathologist diagnostic approach. The entire pipeline from slide to epithelium CIN grade has been designed and developed using deep learning models and imaging techniques to analyze the whole slide image (WSI). The process is as follows: 1) identification of epithelium by filtering the regions extracted from a low-resolution image with a binary classifier network; 2) epithelium segmentation; 3) deep regression for pixel-wise segmentation of epithelium by patch-based image analysis; 4) attention-based CIN classification with localized sequential feature modeling. Deep learning-based nuclei detection by superpixels was performed as an extension of our research. Results from this research indicate an improved performance of CIN assessment over state-of-the-art methods for nuclei segmentation, epithelium segmentation, and CIN classification, as well as the development of a prototype WSI-level tool”--Abstract, page iv

Automatic Segmentation of Cells of Different Types in Fluorescence Microscopy Images

Recognition of different cell compartments, types of cells, and their interactions is a critical aspect of quantitative cell biology. This provides a valuable insight for understanding cellular and subcellular interactions and mechanisms of biological processes, such as cancer cell dissemination, organ development and wound healing. Quantitative analysis of cell images is also the mainstay of numerous clinical diagnostic and grading procedures, for example in cancer, immunological, infectious, heart and lung disease. Computer automation of cellular biological samples quantification requires segmenting different cellular and sub-cellular structures in microscopy images. However, automating this problem has proven to be non-trivial, and requires solving multi-class image segmentation tasks that are challenging owing to the high similarity of objects from different classes and irregularly shaped structures. This thesis focuses on the development and application of probabilistic graphical models to multi-class cell segmentation. Graphical models can improve the segmentation accuracy by their ability to exploit prior knowledge and model inter-class dependencies. Directed acyclic graphs, such as trees have been widely used to model top-down statistical dependencies as a prior for improved image segmentation. However, using trees, a few inter-class constraints can be captured. To overcome this limitation, polytree graphical models are proposed in this thesis that capture label proximity relations more naturally compared to tree-based approaches. Polytrees can effectively impose the prior knowledge on the inclusion of different classes by capturing both same-level and across-level dependencies. A novel recursive mechanism based on two-pass message passing is developed to efficiently calculate closed form posteriors of graph nodes on polytrees. Furthermore, since an accurate and sufficiently large ground truth is not always available for training segmentation algorithms, a weakly supervised framework is developed to employ polytrees for multi-class segmentation that reduces the need for training with the aid of modeling the prior knowledge during segmentation. Generating a hierarchical graph for the superpixels in the image, labels of nodes are inferred through a novel efficient message-passing algorithm and the model parameters are optimized with Expectation Maximization (EM). Results of evaluation on the segmentation of simulated data and multiple publicly available fluorescence microscopy datasets indicate the outperformance of the proposed method compared to state-of-the-art. The proposed method has also been assessed in predicting the possible segmentation error and has been shown to outperform trees. This can pave the way to calculate uncertainty measures on the resulting segmentation and guide subsequent segmentation refinement, which can be useful in the development of an interactive segmentation framework

Medical image segmentation using edge-based active contours.

The main purpose of image segmentation using active contours is to extract the object of interest in images based on textural or boundary information. Active contour methods have been widely used in image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may limit the accuracy of any segmentation method formulated using active contour models. This thesis develops new methods for segmentation of medical images based on the active contour models. Three different approaches are pursued: The first chapter proposes a novel external force that integrates gradient vector flow (GVF) field forces and balloon forces based on a weighting factor computed according to local image features. The proposed external force reduces noise sensitivity, improves performance over weak edges and allows initialization with a single manually selected point. The next chapter proposes a level set method that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the images gradient vector flow field and the evolving contours normal. Finally, chapter 5 presents a framework that is capable of segmenting the cytoplasm of each individual cell and can address the problem of segmenting overlapping cervical cells using edge-based active contours. The main goal of our methodology is to provide significantly fully segmented cells with high accuracy segmentation results. All of the proposed methods are then evaluated for segmentation of various regions in real MRI and CT slices, X-ray images and cervical cell images. Evaluation results show that the proposed method leads to more accurate boundary detection results than other edge-based active contour methods (snake and level-set), particularly around weak edges